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Health Care: Predictive Analytics Enable Better Health Outcomes
Data and predictive analytics when applied to health care has the potential to improve the quality of service. 80 percent of hospital executives said that they expect predictive analytics to help them improve patient care, based on a report from Health Catalyst. Some of the potential ways in which this data analytics might benefit health care include:
- Increase accuracy of diagnoses and targeted treatments, which leads to more accurate, less ‘shotgun-style’ prescription of medication
- Genomic data will be able to aid in the identification of at-risk patients
- Employers may be able to compare the medical characteristics of their workers to identify most effective health plans
- Better application of diagnostics and medicine should lead to better overall outcomes for patients
Dr. Joseph Cacchione, Chair of Operations and Strategy at the Heart and Vascular Institute at Cleveland Clinic, told Health ITAnalytics, that “we’re looking at using predictive analytics to help move us more into the realm of bundled payments or payment for episodes of care, and using administrative and clinical data analytics to create models for clinical outcomes and predicting costs. We think that tying clinical and administrative data systems together is going to be critically important.”
The potential is there, but it may take some time before predictive analytics in healthcare becomes widespread. A survey from Jvion found that just 15 percent of providers use predictive analytics. And a survey by Stoltenberg Consulting found that 34 percent of healthcare professionals struggle with making a case for applying technology like predictive analytics.
Indeed, analytics and big data are an integral part of healthcare and population health management.